Google analytics

month_start <- ym(params$month_start)
month_end <- ym(params$month_end)

# Calculate number of months
months_diff <- floor((month_start %--% month_end) / dmonths(1))

# Create vector with all the relevant months
month_list <- month_start + months(0:months_diff)

# Generate file names using months specified above
filename <- map(month_list, ~ sprintf("../quality_standard/download_analytics/HSC dashboard - Engagement and implementation_All webpages_ links and downloads_Table_%s.csv", as.character(.x) %>% str_remove("-01$")))

# Import data
data <- filename %>% 
    set_names() %>% 
    map_dfr(read_csv, col_types = "ccd", .id = "filename")

# Extract the date from the file name
data <- data %>% 
    mutate(year_month = str_extract(filename, "(?<=Table_)\\d{4}-\\d{2}(?=.csv)") %>% ym(.)) %>% 
    select(-filename)

# Filter for relevant file rows
selected <- data %>% 
    filter(str_detect(File, params$file_regex)) %>% 
    select(year_month, everything()) %>% 
    rename(page_path = 'Page path',
           file = File,
           downloads = Downloads)

# Save
write_csv(selected, sprintf('./output/%s_%s_to_%s.csv', params$file_regex, params$month_start, params$month_end))

# Present raw data
selected %>% 
    datatable(.,
          filter = "top",
          colnames = c("Month" = "year_month",
                       "Download page" = "page_path",
                       "File URL" = "file",
                       "Downloads" = "downloads"),
          rownames = FALSE,
          extensions = 'Buttons', 
          options = list(
              dom = 'Bfrtip',
              buttons = c('csv')
  ))

Monthly downloads of the Quality improvement resource

total_downloads <- selected %>% 
    group_by(year_month) %>% 
    summarise(downloads = sum(downloads))

total_downloads %>% 
    mutate(year_month = zoo::as.yearmon(year_month)) %>% 
    rename("Month" = year_month,
           "Downloads" = downloads) %>% 
    kbl() %>% 
    kable_styling(bootstrap_options = c("striped", "hover"))

Month	Downloads
Jun 2019	131
Jul 2019	127
Aug 2019	90
Sep 2019	84
Oct 2019	118
Nov 2019	146
Dec 2019	76
Jan 2020	120
Feb 2020	122
Mar 2020	81
Apr 2020	61
May 2020	56
Jun 2020	64
Jul 2020	80
Aug 2020	79
Sep 2020	73
Oct 2020	55
Nov 2020	48
Dec 2020	68
Jan 2021	109
Feb 2021	124
Mar 2021	145
Apr 2021	132
May 2021	134
Jun 2021	119
Jul 2021	241
Aug 2021	85
Sep 2021	91
Oct 2021	99
Nov 2021	128
Dec 2021	90
Jan 2022	120
Feb 2022	127
Mar 2022	238
Apr 2022	88
May 2022	126
Jun 2022	100

fig <- total_downloads %>% 
    mutate(year_month = zoo::as.yearmon(year_month)) %>% 
    ggplot(aes(x = year_month, y = downloads)) +
    geom_line() +
    labs(x = "Month",
         y = "Downloads",
         title = paste0("Total monthly downloads of ", params$file_regex)) +
    scale_y_continuous(expand = c(0,0),
                       limits = c(0, NA))
    

fig %>% ggplotly()